Evidence map›Paper›PMID 39304486›Full record

ArticleJournal of pediatric surgery2025

Development and Validation of a Bayesian Network Predicting Intubation Following Hospital Arrival Among Injured Children.

Travis M Sullivan, Mary S Kim, Genevieve J Sippel, Waverly V Gestrich-Thompson, Caroline G Melhado, Kristine L Griffin, Suzanne M Moody, Rajan K Thakkar, Meera Kotagal, Aaron R Jensen and 1 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pediatric surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Travis M SullivanDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA.
Mary S KimDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA.
Genevieve J SippelDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA.
Waverly V Gestrich-ThompsonDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA.
Caroline G MelhadoDepartment of Surgery, University of California San Francisco, San Francisco, CA, USA.
Kristine L GriffinDivision of Pediatric Surgery, Nationwide Children's, Columbus, OH, USA.
Suzanne M MoodyDivision of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Rajan K ThakkarDivision of Pediatric Surgery, Nationwide Children's, Columbus, OH, USA.
Meera KotagalDivision of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Aaron R JensenDepartment of Surgery, University of California San Francisco, San Francisco, CA, USA; Division of Pediatric Surgery, UCSF Benioff Children's Hospitals, San Francisco, CA, USA.
Randall S BurdDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA. Electronic address: rburd@childrensnational.org.

Funding

Intention-aware Recommender System for Improving Trauma Resuscitation OutcomesR01LM011834 · NLM · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., MARSIC, IVAN · 2014 to 2023
$4.7M
NLM NIH HHS R01 LM011834
6 · The paper itself

Abstract

backgroundInadequate airway management can contribute to preventable trauma deaths. Current machine learning tools for predicting intubation in trauma are limited to adult populations and include predictors not readily available at the time of patient arrival. We developed a Bayesian network to predict intubation in injured children and adolescents using observable data available upon or immediately after patient arrival.

methodsWe obtained patient demographic, injury, resuscitation, and transportation characteristics from trauma registries from four American College of Surgeons-verified level 1 pediatric trauma centers from January 2010 through December 2021. We trained and validated a Bayesian network to predict emergent intubation after pediatric injury. We evaluated model performance using the area under the receiver operating and calibration curves.

resultsThe final model, TITAN (Timing of Intubation in Trauma Analysis Network), incorporated five factors: Glasgow Coma Scale, mechanism of injury, injury type (e.g., penetrating, blunt), systolic blood pressure, and age. The model achieved an area under the receiver operating characteristic curve of 0.83 (95% CI 0.80, 0.85) and had a calibration curve slope of 0.98 (95% CI 0.67, 1.29). TITAN had high specificity (98%), negative predictive value (97%), and accuracy (96%) at a binary probability threshold of 22.6%.

conclusionThe TITAN Bayesian network predicts the risk of intubation in pediatric trauma patients using five factors that are observable early in trauma resuscitation. Prospective validation of the model performance with patient outcomes is needed to assess real-life application benefits and risks. LEVEL OF EVIDENCE: Prognostic and Epidemiological, Level III.

Indexed as

Intubation, IntratrachealWounds and InjuriesAdolescentBayes TheoremChildChild, PreschoolFemaleGlasgow Coma ScaleHumansInfantMachine LearningMaleRegistriesROC CurveTrauma CentersAirwayBayesian predictionInjuryIntubationPediatricsTrauma

Identifiers

PMID39304486
PMCPMC11745935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.